Design of a Clinical Decision Support System Framework for the Diagnosis and Prediction of Hepatitis B

International Journal of Computer Applications Technology and Research
Volume 4– Issue 1, 36 - 41, 2015, ISSN:- 2319–8656
www.ijcat.com 36
Design of a Clinical Decision Support System
Framework for the Diagnosis and Prediction of
Hepatitis B
Adekunle Y.A
Department of Computer
Science,
Babcock University,
Ilishan-Remo, Ogun State,
Nigeria
Abstract: This paper proposes an adaptive framework for a Knowledge Based Intelligent Clinical Decision Support System for the
prediction of hepatitis B which is one of the most deadly viral infections that has a monumental effect on the health of people afflicted
with it and has for long remained a perennial health problem affecting a significant number of people the world over. In the framework
the patient information is fed into the system; the Knowledge base stores all the information to be used by the Clinical Decision
Support System and the classification/prediction algorithm chosen after a thorough evaluation of relevant classification algorithms for
this work is the C4.5 Decision Tree Algorithm with its percentage of correctly classified instances given as 61.0734%; it searches the
Knowledge base recursively and matches the patient information with the pertinent rules that suit each case and thereafter gives the
most precise prediction as to whether the patient is prone to hepatitis B or not. This approach to the prediction of hepatitis B provides a
very potent solution to the problem of determining if a person has the likelihood of developing this dreaded illness or is almost not
susceptible to the ailment.
Keywords: Hepatitis , Clinical Decision Support System (CDSS), Medical Decision Support System (MDSS), Artificial Intelligence
(AI), K Nearest Neighbor (K-NN), Decision Trees (DT), Support Vector Machine (SVM) and Sequential Minimal Optimization
(SMO)
1. INTRODUCTION
In recent times, the development of intelligent decision
making applications is fast gaining ground. This concept is
known as Artificial Intelligence (AI). Artificial Intelligence
has different sub-fields which include expert systems,
machine vision, machine learning and natural language
processing amongst others.
A Decision Support System is an interactive computer-based
system intended to help decision makers utilize data and
models in order to identify and solve problems and make
decisions [1]. According to the Clinical Decision Support
(CDS) Roadmap project, CDS is ―providing clinicians,
patients, or individuals with knowledge and person-specific or
population information, intelligently filtered or present at
appropriate times, to foster better health processes, better
individual patient care, and better population health.‖
A Clinical Decision Support System (CDSS) is an active
knowledge system, where two or more items of patient data
are used to generate case-specific recommendation(s) [2].
This implies that a CDSS is a decision support system (DSS)
that uses knowledge management to achieve clinical advice
for patient care based on some number of items of patient
data. This helps to ease the job of healthcare practitioners,
especially in areas where the number of patients is
overwhelming.
Hepatitis B is a viral disease process caused by the hepatitis B
virus (HBV). The virus is endemic throughout the world. It is
shed in all body fluids by individuals with acute or chronic
infection. When transmission occurs from mother to child or
between small children during play, the infection nearly
always becomes chronic. By contrast, when transmission
occurs in adolescents/adults—usually via sexual contact,
contaminated needles or other sharp objects, and less often
from transfusion of blood products—the infection usually
resolves unless the individual is immunocompromised.
Two billion people worldwide have serologic evidence of past
or present HBV infection, and 350 million are chronically
infected and at risk of developing HBV-related liver disease.
Some 15–40% of chronically infected patients will develop
cirrhosis, progressing to liver failure and/or HCC. HBV
infection accounts for 500,000–1,200,000 deaths each year.
Health-care workers remain an at-risk group due to the risk
of needlestick injury, and they should therefore all be
vaccinated before employment.
Individuals chronically infected with HBV are at increased
risk of developing cirrhosis, leading to hepatic
decompensation and hepatocellular carcinoma (HCC).
Although most patients with chronic HBV infection do not
develop hepatic complications, there is the likelihood that
serious illness can develop during their lifetime, and it is more
likely to occur in men.
Every individual chronically infected with HBV provides
an avenue for further cases to be prevented. It is expedient to
take the time needed to educate patients and to explain the
risks that the infection poses to the patients themselves and to
others.
Hepatitis B vaccination is highly efficacious, and universal
vaccination at a tender age is desirable. At the very least,
vaccination should be offered to all individuals who are at
risk. Pregnant women ought to be screened for hepatitis B
before delivery, as this offers an opportunity to prevent
another generation of chronically infected persons.
HBV-related liver injury is majorly caused by immune-
mediated mechanisms, mediated via cytotoxic T-lymphocyte

www.ijcat.com 37
lysis of infected hepatocytes. The precise pathogenic
mechanisms responsible for the HBV-associated acute and
chronic necroinflammatory liver disease and the viral and/or
host determinants of disease severity have only recently been
established [3]. The immune response of the host to HBV-
related antigens is important in determining the result of acute
HBV infection. The strength of the immune response of the
host is critical for clearing the virus, but this simultaneously
causes liver injury (i.e., a form of ―hepatitis‖ manifested by a
rise in transaminases occurs before clearance of the virus can
be achieved). Those who become chronically infected are not
able to sustain an immune response to HBV and thus undergo
intermittent episodes of hepatocyte destruction (hepatitis).
Most studies of acute HBV infection are only initiated after
the onset of symptoms, so that the critical early events
following HBV infection go without notice. A recent study
serially profiled the genomic changes during viral entry,
spread, and clearance of the virus and showed that HBV does
not induce any interferon-regulated genes in the early phase of
the infection. Additionally, no genes were up-regulated or
down-regulated in the lag phase of the infection or during the
phase of viral spread. This suggests that HBV may not induce
the intrahepatic innate immune response. Thus, HBV may be
a ―stealth‖ virus early in the infection.
When neonates are infected during childbirth if their mother is
HBeAg-positive, immune tolerance is induced as the fetus
becomes tolerized to the e antigen, a soluble viral protein that
crosses the placenta in utero. This immune-tolerant phase
goes on for years to decades. Children born to mothers who
are HBeAg-negative but have ongoing viral replication more
often develop an acute hepatitis in the neonatal period, which
is cleared by the infant. However, the infectivity of many
women who are HBeAg-negative is often very low, so that
only about 20% transmit hepatitis B to their offspring.
Table 1. Acute hepatitis B infection: the risk of chronicity
is related to age at primary infection (Source: Neth,
2006)
Figure 1. Chronic hepatitis B infection: phases of infection
(Source: Buster, 2006)
Most cases of chronic hepatitis B in the reactivation phase are
HBeAg-negative, but a few patients may be HBeAg-positive
(Figure 2). The rates of progression to cirrhosis and
hepatocellular carcinoma, with the associated mortality rates,
are shown in Figure
2.
Figure 2. Progression to cirrhosis and hepatocellular
carcinoma, with mortality rates (Source: Hepatol et al, 2003)

www.ijcat.com 38
2. RELATED WORKS
2.1 Decision Support System for Heart
Disease Based on sequential Minimal
Optimization in Support Vector Machine
Here, Vadicherla & Sonawane (2013), the proponents of this
system claim that computer based Medical Decision Support
System (MDSS) can be useful for the physicians with its fast
and accurate decision making process. They opined that
predicting the existence of heart disease accurately, results in
saving the lives of patients followed by proper treatment.
Their objective was to present a MDSS for heart disease
classification based on sequential minimal optimization
(SMO) technique (which incorporated its features like high
accuracy and high speed) in support vector machine (SVM).
In using this method, they illustrated the UCI (University
College Irvine) machine learning repository data of Cleveland
heart disease database and consequently trained the SVM by
using SMO technique. Hence, they also claim that given the
ease of use and better scaling with the training set size, SMO
is a strong candidate for becoming the standard SVM training
algorithm. Training a SVM requires the solution of a very
large QP (Quantum Platform) optimization problem. SMO
algorithm breaks this large optimization problem into small
sub-problems. Both the training and testing phases give the
accuracy on each record. The results proved that the MDSS is
able to carry out heart disease diagnosis accurately in a fast
way and it was reported to show good ability of prediction on
a large dataset.
2.2 Data Mining in Clinical Decision
Support Systems for Diagnosis and
Treatment of Heart Disease
According to Amin, Agarwal & Beg (2013) medical errors are
both costly and harmful. Medical errors cause thousands of
deaths worldwide each year. Hence, a clinical decision
support system (CDSS) would offer opportunities to reduce
medical errors as well as to improve patient safety. They
affirm that one of the most important applications of such
systems is in diagnosis and treatment of heart diseases (HD).
This is because statistics have shown that heart disease is one
of the leading causes of deaths all over the world (CDC
Report). Data mining techniques have been very effective in
designing clinical support systems because of its ability to
discover hidden patterns and relationships in medical data.
Here, the proponents also undertook a comparative analysis of
the performance and working of six CDSS systems which use
different data mining techniques for heart disease diagnosis.
They conclude by asserting based on their findings that there
is no system to identify treatment options for Heart disease
patients. They further claimed that in spite of having a large
amount of medical data, it lacked in the quality and the
completeness of data thereby creating the need for highly
sophisticated data mining techniques to build up an efficient
decision support system. They claim that even after doing
this, the overall reliability and generalization capability might
still be questionable. Hence, the need to build systems which
will be accurate, reliable as well as reduce cost of treatment
and increase patient care. More so, the building of systems
which are understandable and which could enhance human
decisions are very germane.
2.3 An Intelligent Decision Support System
for the Operating Theater
In 2013 Sperandio, Gomes, Borges, Brito and Almada-Lobo
asserted that decision processes inherent in operating theatre
organization are often subjected to experimentation, which
sometimes lead to far from optimal results. They further
affirm that the waiting lists for surgery had always been a
societal problem, with governments seeking redress with
different management and operational stimulus plans partly
due to the fact that the current hospital information systems
available in Portuguese public hospitals, lack a decision
support system component that could help achieve better
planning solutions. As such they developed an intelligent
decision support system that allows the centralization and
standardization of planning processes which improves the
efficiency of the operating theater and tackles the fragile
situation of waiting lists for surgery. The intelligence of the
system is derived from data mining and optimization
techniques, which enhance surgery duration predictions and
operating rooms surgery schedules.
2.4 Decision Support System for the
Diagnosis of Schizophrenia Spectrum
Disorders.
In 2013, Kahn developed a decision support system for the
diagnosis of schizophrenia spectrum disorders.
The development of this system is described in four-stages:
knowledge acquisition, knowledge organization, the
development of a computer-assisted model, and the evaluation
of the system’s performance. The knowledge is extracted
from an expert through open interviews. These interviews
aimed at exploring the expert’s diagnostic decision making
process for the diagnosis of schizophrenia. A graph
methodology was employed to identify the elements involved
in the reasoning process. Knowledge was first organized and
modeled by means of algorithms and then transferred to a
computational model created by the covering approach. The
performance assessment involved the comparison of the
diagnosis of 38 clinical vignettes between an expert and the
decision support system. The results showed a relatively low
rate of misclassification (18-34%) and a good performance by
the decision support system in the diagnosis of schizophrenia,
with an accuracy of 66-82%.
3. CLINICAL DECISION SUPPORT
SYSTEM (CDSS)
The clinical decision support system is another example of a
knowledge based system. A clinical decision support system
is an active knowledge system where two or more items of
patient data are used to generate case specific
recommendations [2].
3.1 Target Area of care
CDSSs assist doctors in assessing various clinical issues from
accurate diagnosis of a particular disease to the treatment of
the disease. The general target areas of care for CDSS are:
Preventive care which has to do with screening and disease
management
Diagnosis which is done based on the patients’ signs and
symptoms
Follow-up management which has to do with frequent
checkups
Hospital Provider Efficiency [8].

www.ijcat.com 39
3.2 System Design
The system design for CDSS will usually include the
following subsystems:
Communication which handles notification and alerts
Knowledge discovery which deals with rules and regulations
Knowledge repository which contains problem solving
knowledge [9].
3.3 Factors leading to successful CDSS
implementation
The following under listed factors lead to the successful
implementation of CDSS:
Simple, user friendly interface
Automated decision support
Timely result
Workflow integration
Continuous Knowledge-base and update support [10].
4. PATTERN CLASSIFICATION
METHODS
Pattern classification refers to the theory and algorithms of
assigning abstract objects into distinct categories, where these
categories are typically known in advance. For this research,
the pattern classification methods considered are Decision
Trees (DTs), K-Nearest Neighbor (KNN), Naïve bayes
Classifier and Support Vector Machine (SVM).
4.1 Decision Trees
A decision tree consists of a root node, branch nodes and leaf
nodes. The tree begins with a root node, then further splits
into branch nodes and each node represents a choice among
various alternatives. The tree then terminates with leaf nodes
which are un-split nodes that represent a decision [11]. The
classification of decision trees are carried out in two phases:
Tree Building or top down: This is computationally intensive
and requires the tree to be recursively partitioned until all the
data items belong to the same class.
Tree pruning or bottom top: It is conducted to improve the
prediction and classification of the algorithm and minimize
the effects of over-fitting which may lead to misclassification
of errors [12].
Some notable decision tree algorithms include Classification
and Regression Trees (CART), Iterative Dichotomiser 3
(ID3), C4.5 and C5.0.
The advantages of decision trees include:
 They are easy to interpret and comprehend
 They can handle both metric and non-metric data as
well as missing values which are frequently
encountered in clinical studies.
 Little data preparation is required since data does
not need to be normalized.
 They can handle data in a short time frame.
 They can be developed using common statistical
techniques.
The disadvantages associated with decision trees include:
 They can over fit the data and create complex trees
that may not generalize well.
 A small change in the size of a dataset could result
in a completely different tree
4.2 K-Nearest Neighbor
K-Nearest Neighbor (k-NN) is instance based
learning for classifying objects based on closest training
examples in the feature space. It is a type of lazy learning
where the function is only approximated locally and all
computations are deferred until classification. The k-nearest
neighbor algorithm is amongst the simplest of all machine
learning algorithms: an object is classified by a majority vote
of its neighbors, with the object being assigned to the class
most common amongst its k nearest neighbors. If k=1, then
the object is simply assigned to the class of its nearest
neighbor. The k-NN algorithm uses all labeled training
instances as a model of the target function. During the
classification phase, k-NN uses a similarity-based search
strategy to determine a locally optimal hypothesis function.
Test instances are compared to the stored instances and are
assigned the same class label as the k most similar stored
instances.
4.3 Bayes Classifier
A Bayesian network is a model that encodes
probabilistic relationships among variables of interest. This
technique is generally used for intrusion detection in
combination with statistical schemes, a procedure that yields
several advantages, including the capability of encoding
interdependencies between variables and of predicting events,
as well as the ability to incorporate both prior knowledge and
data. However, a serious disadvantage of using Bayesian
networks is that their results are similar to those derived from
threshold-based systems, while considerably higher
computational effort is required.
4.4 Support Vector Machine
Support Vector Machines have been proposed as a novel
technique for intrusion detection. An SVM maps input (real-
valued) feature vectors into a higher-dimensional feature
space through some nonlinear mapping. SVMs are developed
on the principle of structural risk minimization. Structural risk
minimization seeks to find a hypothesis (h) for which one can
find lowest probability of error whereas the traditional
learning techniques for pattern recognition are based on the
minimization of the empirical risk, which attempt to optimize
the performance of the learning set. Computing the hyper
plane to separate the data points i.e. training an SVM leads to
a quadratic optimization problem. The implementation of
SVM intrusion detection system has two phases which are
training and testing. SVMs can learn a larger set of patterns
and be able to scale better, because the classification
complexity does not depend on the dimensionality of the
feature space. SVMs also have the ability to update the
training patterns dynamically whenever there is a new pattern
during classification.
Table 1. Comparison of Some Pattern Classification
Algorithms (Source: Patel et al, 2012)
Classif
ier
Method Paramete
rs
Advantage
s
Disadvant
ages
Suppor
t
Vector
Machi
ne
A
support
vector
machine
construct
s a hyper
plane or
set of
hyper
planes in
a high or
infinite
dimensio
nal
The
effectiven
ess of
SVM lies
in the
selection
of kernel
and soft
margin
parameter
s. For
kernels,
different
pairs of
1. Highly
Accurate
2. Able to
model
complex
nonlinear
decision
boundaries
3. Less
prone to
over fitting
than other
methods
1. High
algorithmi
c
complexit
y and
extensive
memory
requireme
nts of the
required
quadratic
programm
ing in
large-scale

www.ijcat.com 40
space,
which
can be
used for
classifica
tion,
regressio
n or other
tasks.
(C, γ)
values are
tried and
the one
with the
best cross-
validation
accuracy
is picked.
Trying
exponenti
ally
growing
sequences
of C is a
practical
method to
identify
good
parameter
s.
tasks.
2. The
choice of
the kernel
is difficult
3. The
speed both
in training
and testing
is slow.
K
Neares
t
Neighb
our
An
object is
classified
by a
majority
vote of
its
neighbou
rs, with
the
object
being
assigned
to the
class
most
common
amongst
its k
nearest
neighbou
rs (k is a
positive
integer).
If k = 1,
then the
object is
simply
assigned
to the
class of
its
nearest
neighbou
r.
Two
parameter
s are
considere
d to
optimize
the
performan
ce of the
kNN, the
number k
of nearest
neighbour
and the
feature
space
transform
ation.
1.
Analyticall
y tractable.
2. Simple
in
implement
ation
3. Uses
local
informatio
n, which
can yield
highly
adaptive
behaviour
4. Lends
itself very
easily to
parallel
implement
ations
1. Large
storage
requireme
nts.
2. Highly
susceptibl
e to the
curse of
dimension
ality.
3. Slow in
classifying
test tuples.
Bayesi
an
Metho
d
Based on
the rule,
using the
joint
probabilit
ies of
sample
observati
ons and
classes,
the
In Bayes,
all model
parameter
s (i.e.,
class
priors and
feature
probabilit
y
distributio
ns) can be
1. Naïve
Bayesian
classifier
simplifies
the
computatio
ns. 2.
Exhibit
high
accuracy
and speed
1 The
assumptio
ns made in
class
conditiona
l
independe
nce.
2. Lack of
available
probabilit
algorithm
attempts
to
estimate
the
condition
al
probabilit
ies of
classes
given an
observati
on.
approxima
ted with
relative
frequencie
s from the
training
set.
when
applied to
large
databases.
y data.
Decisi
on
Tree
Decision
tree
builds a
binary
classifica
tion tree.
Each
node
correspo
nds to a
binary
predicate
on one
attribute;
one
branch
correspo
nds to the
positive
instances
of the
predicate
and the
other to
the
negative
instances
.
Decision
Tree
Induction
uses
parameter
s like a set
of
candidate
attributes
and an
attribute
selection
method.
1.
Constructio
n does not
require any
domain
knowledge.
2. Can
handle high
dimensiona
l data.
3.
Representa
tion is easy
to
understand.
4. Able to
process
both
numerical
and
categorical
data.
1. Output
attribute
must be
categorica
l.
2. Limited
to one
output
attribute.
3.
Decision
tree
algorithms
are
unstable.
4. Trees
created
from
numeric
datasets
can be
complex.
5. METHODOLOGY
A very comprehensive dataset (Velvet, 2008) consisting of
100,000 instances compiled from the UCI (University of
California, Irvine) data repository was used. This dataset was
translated into the Attribute Relational File Format (ARFF)
which is one of the file formats recognized by the WEKA
(Waikato Environment for Knowledge Analysis) software in
which the distinct genotypic attributes used for this work were
highlighted.
The dataset was then induced with Classification algorithms
namely C4.5 decision trees, Support Vector Machine (SVM),
K-Nearest neighbor algorithm and Bayes Classifier
Algorithm. The Classification algorithms were evaluated
using the Waikato Environment for Knowledge Analysis
software version 3.6.7 based on the percentage of correctly
classified instances with the C4.5 decision trees having
61.0734%, the Support Vector Machine (SVM) algorithm
had 50.0515%, the Bayes Classifier Algorithm had 50.2045%
and the K-Nearest Neighbor algorithm had 50.1235%. Sequel
to the result obtained from this evaluation, the C4.5 decision
trees turn out as the Classification algorithm with the highest
accuracy for this research. Thereafter, a decision tree program
was written in Java with 38 lines of code for the core program

www.ijcat.com 41
to implement the C4.5 decision tree algorithm that will
provide the requisite intelligence for this Clinical decision
support system and help it make the right decisions promptly
when supplied with patient information. The C4.5 decision
tree algorithm will be embedded in the
classification/prediction algorithm section of the clinical
decision support system.
6. CONCLUSION AND
RECOMMENDATION
This research work finds its significance in all parts of the
world where people live with the health challenge caused by
the hepatitis B virus, thus it is very germane as it provides a
sort of panacea to the eventual development of the condition
known as hepatitis B for people who are susceptible to the
condition, hence they can be aware of their susceptibility
ahead of time and can be able to take the necessary
precautionary measures to forestall their development of the
illness, thus saving them from the trauma they would have
inevitably suffered.
The research is a milestone in the sub-field of health
informatics as it provides a readily available Clinical Decision
Support System to serve as a reliable assistant to the medical
practitioners that are more often than not burdened by the
overwhelming and seemingly intimidating number of patients
they need to attend to routinely. This has culminated in a lot
of fatal errors on the part of the medical practitioners which
has led to the loss of innocent lives hence, the introduction,
consequent adoption and deployment of this Knowledge
Based Intelligent Clinical Decision Support System for the
prediction of hepatitis B becomes expedient especially in the
third world countries, the vast majority of who lag behind in
terms of technological innovations and advancement and as a
result are alien to the terrific results gotten from the use of
these clinical decision support systems.
For further work another enthusiastic researcher can go a step
further in this work by introducing other highly efficacious
algorithms that can be used alongside the C4.5 decision tree
algorithm used in this work, so as to have a hybrid system that
will take decisions faster and generate more accurate
decisions than those that will be given by the proposed
system.
7. REFERENCES
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[3] Naumann, H, Scott R.M, Snitbhan R, Bancroft W.H, Alter
H.J & Tingpalapong, M (2006). Experimental transmission of
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Infectious Diseases, 23(8), 27-35.
[4] Vadicherla, D. & Sonawane, S. (2013). Decision support
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